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      "cell_type": "markdown",
      "metadata": {
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        "id": "ADKY4re5Kx-5"
      },
      "source": [
        "##### Copyright 2018 The TensorFlow Authors.\n",
        "\n",
        "Licensed under the Apache License, Version 2.0 (the \"License\");"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "S2AOrHzjK0_L"
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      "outputs": [],
      "source": [
        "#@title Licensed under the Apache License, Version 2.0 (the \"License\"); { display-mode: \"form\" }\n",
        "# you may not use this file except in compliance with the License.\n",
        "# You may obtain a copy of the License at\n",
        "#\n",
        "# https://www.apache.org/licenses/LICENSE-2.0\n",
        "#\n",
        "# Unless required by applicable law or agreed to in writing, software\n",
        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "# See the License for the specific language governing permissions and\n",
        "# limitations under the License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "56dF5DnkKx0a"
      },
      "source": [
        "# Gaussian Process Regression in TensorFlow Probability\n",
        "\n",
        "\u003ctable class=\"tfo-notebook-buttons\" align=\"left\"\u003e\n",
        "  \u003ctd\u003e\n",
        "    \u003ca target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/probability/blob/master/tensorflow_probability/examples/jupyter_notebooks/Gaussian_Process_Regression_In_TFP.ipynb\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" /\u003eRun in Google Colab\u003c/a\u003e\n",
        "  \u003c/td\u003e\n",
        "  \u003ctd\u003e\n",
        "    \u003ca target=\"_blank\" href=\"https://github.com/tensorflow/probability/blob/master/tensorflow_probability/examples/jupyter_notebooks/Gaussian_Process_Regression_In_TFP.ipynb\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" /\u003eView source on GitHub\u003c/a\u003e\n",
        "  \u003c/td\u003e\n",
        "\u003c/table\u003e"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "rTFa-AE9KxFC"
      },
      "source": [
        "In this colab, we explore Gaussian process regression using\n",
        "TensorFlow and TensorFlow Probability. We generate some noisy observations from\n",
        "some known functions and fit GP models to those data. We then sample from the GP\n",
        "posterior and plot the sampled function values over grids in their domains.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "n4-qQf7qZLVI"
      },
      "source": [
        "## Background\n",
        "Let $\\mathcal{X}$ be any set. A *Gaussian process*\n",
        "(GP) is a collection of random variables indexed by $\\mathcal{X}$ such that if\n",
        "$\\{X_1, \\ldots, X_n\\} \\subset \\mathcal{X}$ is any finite subset, the marginal density\n",
        "$p(X_1 = x_1, \\ldots, X_n = x_n)$ is multivariate Gaussian. Any Gaussian\n",
        "distribution is completely specified by its first and second central moments\n",
        "(mean and covariance), and GP's are no exception. We can specify a GP completely\n",
        "in terms of its mean function $\\mu : \\mathcal{X} \\to \\mathbb{R}$ and covariance function\n",
        "$k : \\mathcal{X} \\times \\mathcal{X} \\to \\mathbb{R}$. Most of the expressive power of GP's is encapsulated\n",
        "in the choice of covariance function. For various reasons, the covariance\n",
        "function is also referred to as a *kernel function*. It is required only to be\n",
        "symmetric and positive-definite (see [Ch. 4 of Rasmussen \u0026 Williams](\n",
        "http://www.gaussianprocess.org/gpml/chapters/RW4.pdf)). Below we make use of the\n",
        "ExponentiatedQuadratic covariance kernel. Its form is\n",
        "\n",
        "$$\n",
        "k(x, x') := \\sigma^2 \\exp \\left( \\frac{\\|x - x'\\|^2}{\\lambda^2} \\right)\n",
        "$$\n",
        "\n",
        "where $\\sigma^2$ is called the 'amplitude' and $\\lambda$ the *length scale*.\n",
        "The kernel parameters can be selected via a maximum likelihood optimization\n",
        "procedure.\n",
        "\n",
        "A full sample from a GP comprises a real-valued function over the entire space\n",
        "$\\mathcal{X}$ and is in practice impractical to realize; often one chooses a set of\n",
        "points at which to observe a sample and draws function values at these points.\n",
        "This is achieved by sampling from an appropriate (finite-dimensional)\n",
        "multi-variate Gaussian.\n",
        "\n",
        "Note that, according to the above definition, any finite-dimensional\n",
        "multivariate Gaussian distribution is also a Gaussian process. Usually, when\n",
        "one refers to a GP, it is implicit that the index set is some $\\mathbb{R}^n$\n",
        "and we will indeed make this assumption here.\n",
        "\n",
        "A common application of Gaussian processes in machine learning is Gaussian\n",
        "process regression. The idea is that we wish to estimate an unknown function\n",
        "given noisy observations $\\{y_1, \\ldots, y_N\\}$ of the function at a finite\n",
        "number of points $\\{x_1, \\ldots x_N\\}.$ We imagine a generative process\n",
        "\n",
        "$$\n",
        "\\begin{align}\n",
        "f \\sim \\: \u0026 \\textsf{GaussianProcess}\\left(\n",
        "    \\text{mean_fn}=\\mu(x),\n",
        "    \\text{covariance_fn}=k(x, x')\\right) \\\\\n",
        "y_i \\sim \\: \u0026 \\textsf{Normal}\\left(\n",
        "    \\text{loc}=f(x_i),\n",
        "    \\text{scale}=\\sigma\\right), i = 1, \\ldots, N\n",
        "\\end{align}\n",
        "$$\n",
        "\n",
        "As noted above, the sampled function is impossible to compute, since we would\n",
        "require its value at an infinite number of points. Instead, one considers a\n",
        "finite sample from a multivariate Gaussian.\n",
        "\n",
        "$$\n",
        "  \\begin{gather}\n",
        "    \\begin{bmatrix}\n",
        "      f(x_1) \\\\\n",
        "      \\vdots \\\\\n",
        "      f(x_N)\n",
        "    \\end{bmatrix}\n",
        "    \\sim\n",
        "    \\textsf{MultivariateNormal} \\left( \\:\n",
        "      \\text{loc}=\n",
        "      \\begin{bmatrix}\n",
        "        \\mu(x_1) \\\\\n",
        "        \\vdots \\\\\n",
        "        \\mu(x_N)\n",
        "      \\end{bmatrix} \\:,\\:\n",
        "      \\text{scale}=\n",
        "      \\begin{bmatrix}\n",
        "        k(x_1, x_1) \u0026 \\cdots \u0026 k(x_1, x_N) \\\\\n",
        "        \\vdots \u0026 \\ddots \u0026 \\vdots \\\\\n",
        "        k(x_N, x_1) \u0026 \\cdots \u0026 k(x_N, x_N) \\\\\n",
        "      \\end{bmatrix}^{1/2}\n",
        "    \\: \\right)\n",
        "  \\end{gather} \\\\\n",
        "  y_i \\sim \\textsf{Normal} \\left(\n",
        "      \\text{loc}=f(x_i),\n",
        "      \\text{scale}=\\sigma\n",
        "  \\right)\n",
        "$$\n",
        "\n",
        "Note the exponent $\\frac{1}{2}$ on the covariance matrix: this denotes a\n",
        "Cholesky decomposition. Comptuing the Cholesky is necessary because the MVN is\n",
        "a location-scale family distribution. Unfortunately the Cholesky decomposition\n",
        "is computationally expensive, taking $O(N^3)$ time and $O(N^2)$ space. Much of\n",
        "the GP literature is focused on dealing with this seemingly innocuous little\n",
        "exponent.\n",
        "\n",
        "It is common to take the prior mean function to be constant, often zero. Also,\n",
        "some notational conventions are convenient. One often writes $\\mathbf{f}$ for the\n",
        "finite vector of sampled function values. A number of interesting notations are\n",
        "used for the covariance matrix resulting from the application of $k$ to pairs of\n",
        "inputs. Following [(Quiñonero-Candela, 2005)][QuinoneroCandela2005], we note\n",
        "that the components of the matrix are covariances of function values at\n",
        "particular input points. Thus we can denote the covariance matrix as $K_{AB}$\n",
        "where $A$ and $B$ are some indicators of the collection of function values along\n",
        "the given matrix dimensions.\n",
        "\n",
        "[QuinoneroCandela2005]: http://www.jmlr.org/papers/volume6/quinonero-candela05a/quinonero-candela05a.pdf\n",
        "\n",
        "For example, given observed data $(\\mathbf{x}, \\mathbf{y})$ with implied latent function\n",
        "values $\\mathbf{f}$, we can write\n",
        "\n",
        "$$\n",
        "K_{\\mathbf{f},\\mathbf{f}} = \\begin{bmatrix}\n",
        "  k(x_1, x_1) \u0026 \\cdots \u0026 k(x_1, x_N) \\\\\n",
        "  \\vdots \u0026 \\ddots \u0026 \\vdots \\\\\n",
        "  k(x_N, x_1) \u0026 \\cdots \u0026 k(x_N, x_N) \\\\\n",
        "\\end{bmatrix}\n",
        "$$\n",
        "\n",
        "Similarly, we can mix sets of inputs, as in\n",
        "\n",
        "$$\n",
        "K_{\\mathbf{f},*} = \\begin{bmatrix}\n",
        "  k(x_1, x^*_1) \u0026 \\cdots \u0026 k(x_1, x^*_T) \\\\\n",
        "  \\vdots \u0026 \\ddots \u0026 \\vdots \\\\\n",
        "  k(x_N, x^*_1) \u0026 \\cdots \u0026 k(x_N, x^*_T) \\\\\n",
        "\\end{bmatrix}\n",
        "$$\n",
        "\n",
        "where we suppose there are $N$ training inputs, and $T$ test inputs. The above\n",
        "generative process may then be written compactly as\n",
        "\n",
        "$$\n",
        "\\begin{align}\n",
        "\\mathbf{f} \\sim \\: \u0026 \\textsf{MultivariateNormal} \\left(\n",
        "        \\text{loc}=\\mathbf{0},\n",
        "        \\text{scale}=K_{\\mathbf{f},\\mathbf{f}}^{1/2}\n",
        "    \\right) \\\\\n",
        "y_i \\sim \\: \u0026 \\textsf{Normal} \\left(\n",
        "    \\text{loc}=f_i,\n",
        "    \\text{scale}=\\sigma \\right), i = 1, \\ldots, N\n",
        "\\end{align}\n",
        "$$\n",
        "\n",
        "The sampling operation in the first line yields a finite set of $N$ function\n",
        "values from a multivariate Gaussian -- *not an entire function as in the above\n",
        "GP draw notation*. The second line describes a collection of $N$ draws from\n",
        "*univariate* Gaussians centered at the various function values, with fixed\n",
        "observation noise $\\sigma^2$.\n",
        "\n",
        "With the above generative model in place, we can proceed to consider the\n",
        "posterior inference problem. This yields a posterior distribution over function\n",
        "values at a new set of test points, conditioned on the observed noisy data from\n",
        "the process above.\n",
        "\n",
        "With the above notation in place, we can compactly write the posterior\n",
        "predictive distribution over future (noisy) observations conditional on\n",
        "corresponding inputs and training data as follows (for more details, see §2.2 of\n",
        "[Rasmussen \u0026 Williams](http://www.gaussianprocess.org/gpml/)). \n",
        "\n",
        "$$\n",
        "\\mathbf{y}^* \\mid \\mathbf{x}^*, \\mathbf{x}, \\mathbf{y} \\sim \\textsf{Normal} \\left(\n",
        "    \\text{loc}=\\mathbf{\\mu}^*,\n",
        "    \\text{scale}=(\\Sigma^*)^{1/2}\n",
        "\\right),\n",
        "$$\n",
        "\n",
        "where\n",
        "\n",
        "$$\n",
        "\\mathbf{\\mu}^* = K_{*,\\mathbf{f}}\\left(K_{\\mathbf{f},\\mathbf{f}} + \\sigma^2 I \\right)^{-1} \\mathbf{y}\n",
        "$$\n",
        "\n",
        "and\n",
        "\n",
        "$$\n",
        "\\Sigma^* = K_{*,*} - K_{*,\\mathbf{f}}\n",
        "    \\left(K_{\\mathbf{f},\\mathbf{f}} + \\sigma^2 I \\right)^{-1} K_{\\mathbf{f},*}\n",
        "$$"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "QF_BW8m_c3_d"
      },
      "source": [
        "## Imports"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "cellView": "code",
        "colab": {
          "height": 34
        },
        "colab_type": "code",
        "executionInfo": {
          "elapsed": 10747,
          "status": "ok",
          "timestamp": 1538676119013,
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          },
          "user_tz": 240
        },
        "id": "jw-_1yC50xaM",
        "outputId": "d49daff7-92da-428b-9684-dce4a2d60a55"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Populating the interactive namespace from numpy and matplotlib\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "from mpl_toolkits.mplot3d import Axes3D\n",
        "from tensorflow_probability import distributions as tfd\n",
        "from tensorflow_probability import positive_semidefinite_kernels as tfk\n",
        "\n",
        "%pylab inline\n",
        "# Configure plot defaults\n",
        "plt.rcParams['axes.facecolor'] = 'white'\n",
        "plt.rcParams['grid.color'] = '#666666'\n",
        "%config InlineBackend.figure_format = 'png'"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "cu9S6c7uuvC1"
      },
      "source": [
        "## Example: Exact GP Regression on Noisy Sinusoidal Data\n",
        "Here we generate training data from a noisy sinusoid, then sample a bunch of\n",
        "curves from the posterior of the GP regression model. We use\n",
        "[Adam](https://arxiv.org/abs/1412.6980) to optimize the kernel hyperparameters\n",
        "(we minimize the negative log likelihood of the data under the prior). We\n",
        "plot the training curve, followed by the true function and the posterior\n",
        "samples."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "-YvP5laJwi_Q"
      },
      "outputs": [],
      "source": [
        "def reset_session():\n",
        "  \"\"\"Creates a new global, interactive session in Graph-mode.\"\"\"\n",
        "  global sess\n",
        "  try:\n",
        "    tf.reset_default_graph()\n",
        "    sess.close()\n",
        "  except:\n",
        "    pass\n",
        "  sess = tf.InteractiveSession()\n",
        "\n",
        "reset_session()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "Qrys68xzZE-c"
      },
      "outputs": [],
      "source": [
        "def sinusoid(x):\n",
        "  return np.sin(3 * np.pi * x[..., 0])\n",
        "\n",
        "def generate_1d_data(num_training_points, observation_noise_variance):\n",
        "  \"\"\"Generate noisy sinusoidal observations at a random set of points.\n",
        "\n",
        "  Returns:\n",
        "     observation_index_points, observations\n",
        "  \"\"\"\n",
        "  index_points_ = np.random.uniform(-1., 1., (num_training_points, 1))\n",
        "  index_points_ = index_points_.astype(np.float64)\n",
        "  # y = f(x) + noise\n",
        "  observations_ = (sinusoid(index_points_) +\n",
        "                   np.random.normal(loc=0,\n",
        "                                    scale=np.sqrt(observation_noise_variance),\n",
        "                                    size=(num_training_points)))\n",
        "  return index_points_, observations_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "Tem9p8rUlqQR"
      },
      "outputs": [],
      "source": [
        "# Generate training data with a known noise level (we'll later try to recover\n",
        "# this value from the data).\n",
        "NUM_TRAINING_POINTS = 100\n",
        "observation_index_points_, observations_ = generate_1d_data(\n",
        "    num_training_points=NUM_TRAINING_POINTS,\n",
        "    observation_noise_variance=.1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "ByXndE3pkA4x"
      },
      "outputs": [],
      "source": [
        "# Create the trainable model parameters, which we'll subsequently optimize.\n",
        "# Note that we constrain them to be strictly positive.\n",
        "amplitude = (np.finfo(np.float64).tiny +\n",
        "             tf.nn.softplus(tf.Variable(initial_value=1.,\n",
        "                                        name='amplitude',\n",
        "                                        dtype=np.float64)))\n",
        "length_scale = (np.finfo(np.float64).tiny +\n",
        "                tf.nn.softplus(tf.Variable(initial_value=1.,\n",
        "                                           name='length_scale',\n",
        "                                           dtype=np.float64)))\n",
        "\n",
        "observation_noise_variance = (\n",
        "    np.finfo(np.float64).tiny +\n",
        "    tf.nn.softplus(tf.Variable(initial_value=1e-6,\n",
        "                               name='observation_noise_variance',\n",
        "                               dtype=np.float64)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "GNfCUmnjlXWY"
      },
      "outputs": [],
      "source": [
        "# Create the covariance kernel, which will be shared between the prior (which we\n",
        "# use for maximum likelihood training) and the posterior (which we use for\n",
        "# posterior predictive sampling)\n",
        "kernel = tfk.ExponentiatedQuadratic(amplitude, length_scale)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "EmmImEdDljIg"
      },
      "outputs": [],
      "source": [
        "# Create the GP prior distribution, which we will use to train the model\n",
        "# parameters.\n",
        "gp = tfd.GaussianProcess(\n",
        "    kernel=kernel,\n",
        "    index_points=observation_index_points_,\n",
        "    observation_noise_variance=observation_noise_variance)\n",
        "\n",
        "# This lets us compute the log likelihood of the observed data. Then we can\n",
        "# maximize this quantity to find optimal model parameters.\n",
        "log_likelihood = gp.log_prob(observations_)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "N4ZHVZVDmaf6"
      },
      "outputs": [],
      "source": [
        "# Define the optimization ops for maximizing likelihood (minimizing neg\n",
        "# log-likelihood!)\n",
        "optimizer = tf.train.AdamOptimizer(learning_rate=.01)\n",
        "train_op = optimizer.minimize(-log_likelihood)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "height": 85
        },
        "colab_type": "code",
        "executionInfo": {
          "elapsed": 4035,
          "status": "ok",
          "timestamp": 1538676127987,
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        },
        "id": "4swUVjI0DZY4",
        "outputId": "f9bb88d2-7e67-4d48-d695-87c4967e648c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Trained parameters:\n",
            "amplitude: 0.737212035424\n",
            "length_scale: 0.120468090569\n",
            "observation_noise_variance: 0.0978894813992\n"
          ]
        }
      ],
      "source": [
        "# Now we optimize the model parameters.\n",
        "num_iters = 1000\n",
        "# Store the likelihood values during training, so we can plot the progress\n",
        "lls_ = np.zeros(num_iters, np.float64)\n",
        "sess.run(tf.global_variables_initializer())\n",
        "for i in range(num_iters):\n",
        "  _, lls_[i] = sess.run([train_op, log_likelihood])\n",
        "\n",
        "[amplitude_,\n",
        " length_scale_,\n",
        " observation_noise_variance_] = sess.run([\n",
        "    amplitude,\n",
        "    length_scale,\n",
        "    observation_noise_variance])\n",
        "print('Trained parameters:'.format(amplitude_))\n",
        "print('amplitude: {}'.format(amplitude_))\n",
        "print('length_scale: {}'.format(length_scale_))\n",
        "print('observation_noise_variance: {}'.format(observation_noise_variance_))"
      ]
    },
    {
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      "execution_count": 0,
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      "outputs": [
        {
          "data": {
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OHTtUWlrqak9PTz+nE3fs2FGSZBhGlfb09HRdc801slqtio2NVYcOHZSRkSHD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\nKIkAAAAASUVORK5CYII=\n",
            "text/plain": [
              "\u003cmatplotlib.figure.Figure at 0x7f4d6ba3b9d0\u003e"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Plot the loss evolution\n",
        "plt.figure(figsize=(12, 4))\n",
        "plt.plot(lls_)\n",
        "plt.xlabel(\"Training iteration\")\n",
        "plt.ylabel(\"Log marginal likelihood\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "1DOkwqQEsXVs"
      },
      "outputs": [],
      "source": [
        "# Having trained the model, we'd like to sample from the posterior conditioned\n",
        "# on observations. We'd like the samples to be at points other than the training\n",
        "# inputs.\n",
        "predictive_index_points_ = np.linspace(-1.2, 1.2, 200, dtype=np.float64)\n",
        "# Reshape to [200, 1] -- 1 is the dimensionality of the feature space.\n",
        "predictive_index_points_ = predictive_index_points_[..., np.newaxis]\n",
        "\n",
        "gprm = tfd.GaussianProcessRegressionModel(\n",
        "    kernel=kernel,  # Reuse the same kernel instance, with the same params\n",
        "    index_points=predictive_index_points_,\n",
        "    observation_index_points=observation_index_points_,\n",
        "    observations=observations_,\n",
        "    observation_noise_variance=observation_noise_variance,\n",
        "    predictive_noise_variance=0.)\n",
        "\n",
        "# Create op to draw  50 independent samples, each of which is a *joint* draw\n",
        "# from the posterior at the predictive_index_points_. Since we have 200 input\n",
        "# locations as defined above, this posterior distribution over corresponding\n",
        "# function values is a 200-dimensional multivariate Gaussian distribution!\n",
        "num_samples = 50\n",
        "samples = gprm.sample(num_samples)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "height": 291
        },
        "colab_type": "code",
        "executionInfo": {
          "elapsed": 1147,
          "status": "ok",
          "timestamp": 1538676131028,
          "user": {
            "displayName": "",
            "photoUrl": "",
            "userId": ""
          },
          "user_tz": 240
        },
        "id": "1XgqrfsSub15",
        "outputId": "156ac390-0e72-40b8-96b8-e63338844643"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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vzi06nxQ3EtmX/clncP+rqDu93QHyq5s+ff9g/VzO05Aolrvxf/0nF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%0AJUmSpAYM1JIkSVIDlwFxbniFNHM9UAAAAABJRU5ErkJggg==",
            "text/plain": [
              "\u003cmatplotlib.figure.Figure at 0x7f4d6aa52250\u003e"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Draw samples and visualize.\n",
        "samples_ = sess.run(samples)\n",
        "\n",
        "# Plot the true function, observations, and posterior samples.\n",
        "plt.figure(figsize=(12, 4))\n",
        "plt.plot(predictive_index_points_, sinusoid(predictive_index_points_),\n",
        "         label='True fn')\n",
        "plt.scatter(observation_index_points_[:, 0], observations_,\n",
        "            label='Observations')\n",
        "for i in range(num_samples):\n",
        "  plt.plot(predictive_index_points_, samples_[i, :], c='r', alpha=.1,\n",
        "           label='Posterior Sample' if i == 0 else None)\n",
        "leg = plt.legend(loc='upper right')\n",
        "for lh in leg.legendHandles: \n",
        "    lh.set_alpha(1)\n",
        "plt.xlabel(r\"Index points ($\\mathbb{R}^1$)\")\n",
        "plt.ylabel(\"Observation space\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "aZe4H-7jy0hR"
      },
      "source": [
        "*Note: if you run the above code several times, sometimes it looks great and\n",
        "other times it looks terrible! The maximum likelihood training of the parameters\n",
        "is quite sensitive and sometimes converges to poor models. The best approach\n",
        "is to use MCMC to marginalize the model hyperparameters.*"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "name": "Gaussian Process Regression in TensorFlow Probability",
      "provenance": [
        {
          "file_id": "1w4FZslgiCCMWUT7NbGi6fnSPY6YhTSDJ",
          "timestamp": 1537383195517
        }
      ],
      "version": "0.3.2"
    },
    "kernelspec": {
      "display_name": "Python 2",
      "name": "python2"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}
